Upload app.py

app.py

@@ -46,7 +46,7 @@ import gradio as gr
 import torch
 import os
 from transformers import BlipForConditionalGeneration, BlipProcessor, GenerationConfig
-
+print(torch.__version__)
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 _MODEL_PATH = 'IDEA-CCNL/Taiyi-BLIP-750M-Chinese'
@@ -76,7 +76,7 @@ def inference(raw_image, model_n, strategy):
                 # Nucleus sampling，即 top-p sampling，只保留累积概率大于 p 的词，然后重新归一化，得到一个新的概率分布，再从中采样，这样可以保证采样的结果更多样
                 config = GenerationConfig(
                     do_sample=True,
-                    top_p=0.9,
+                    top_p=0.8,
                     max_length=50,
                     min_length=5,
                 )